Seamlessly integrate LLMs into scikit-learn.

🔍 AI-powered diagnosis for Scikit-learn models: Detect overfitting, data leakage, class imbalance & more with LLM-generated insights

Zero/few shot learning components for scikit-learn pipelines with LLMs and transformers.

Stack & Orchestrate MCP Tools — The Scikit-Learn-Pipeline Way , For LLMs

Scikit LLM Text Classification Streamlit App repository from AI Anytime YouTube channel.

skfeaturellm is a Python library that brings the power of Large Language Models (LLMs) to feature engineering for tabular data, wrapped in a familiar scikit-learn–style API.

AI MLOps Project – A production-grade MLOps pipeline for scalable, reproducible, and cloud-ready machine learning with Spark, scikit-learn, MLflow, and LLM-powered analytics.

The "LokoAI-Enhanced: ScikitLLM Extension" project aims to bolster the capabilities of LokoAI by integrating the advanced functionalities of the ScikitLLM library. This extension intends to enhance LokoAI's feature set by incorporating the specialized tools and algorithms offered by ScikitLLM.

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A news aggregator and summarizer powered by NLTK and Scikit-learn (not LLM)

Notebooks for ML Tasks w/ Scikit and LLMs using Cohere, HuggingFace, LangChain, and OpenAI

A Python module for building data transformation pipelines combining the power of scikit-learn and Large Language Models (LLMs)

Creating an MCP Server to allow LLM to work with datasets, by using pandas, numpy, scikit-learn, tensorflow, and pytorch.

A state-aware multimodal assistant integrating LLMs (Gemini-Flash) with custom Scikit-learn models for sentiment analysis and logistics forecasting.

Python projects about LLM and ML use cases. I am using modules such as Pandas, Numpy, Plotly, scikit-learn, Transformers, Flask, JSON, etc. to analyze data, predict, generate insights and create text from models such as LLMs, linear regression, assembly methods, etc. Server- Front-End using Flask

A hybrid recommendation engine built with Python, Pandas, Scikit-learn, and Flask, combining SVD matrix factorization and cosine similarity for personalized movie recommendations. Features include real-time REST API, LLM-powered natural language queries, evaluation with RMSE / Precision@k, optional Streamlit UI, and Docker + CI/CD support.

Scikit-LLM Docs

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A Text Classification Web App using Scikit LLM and Streamlit

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A sophisticated Digital Twin for rainwater harvesting that leverages Gemini 2.0 Flash (LLM) for generative engineering intelligence and Scikit-Learn for predictive water consumption modeling.

AI-driven Requirements Analysis System built with Flask, Scikit-Learn, and OpenRouter. Automatically scores SRS documents and provides deterministic LLM-powered rewrites for quality assurance.

An automated system that optimizes machine learning models using LLM-guided improvements. It iterates over scikit-learn and XGBoost classifiers, dynamically applying LLM-suggested improvements to models and hyperparameters, ensuring optimal model selection for classification tasks.

A smart book recommender using LLMs, NLP, and FAISS to suggest books based on semantic similarity. Built with Python, Hugging Face, Gradio, and scikit-learn in Jupyter.

Full-stack ML app with LLM integration & real-time WebSocket streaming | Python, React, Flask, AWS Bedrock, Socket.IO, scikit-learn | <5% prediction accuracy, AI-generated training plans, 15+ Strava features

a spam classification system by preprocessing SMS data, generating TF-IDF features, and applying CNN, BiLSTM, and LLM-assisted XGBoost models. (Tool: Python, Scikit-learn, TensorFlow/Keras, XGBoost, Pandas, NumPy, NLTK)

6-class text authorship detection pipeline for human and LLM-generated text using TF-IDF, stylometric features, and stacked scikit-learn/LightGBM models for the MALTO Hackathon 2026 (F1: 0.9567).

AI-powered business intelligence system combining RAG, LLMs, and machine learning for retail analytics. Features customer segmentation, churn prediction, sales forecasting, and intelligent document querying. Built with LangChain, FAISS, and scikit-learn.

Code and experiment outputs for our DECLARE MOC2025 paper on an LLM-based labeling workflow with XAI (SHAP) and human-in-the-loop QA. Built from a technical-debt detection case study using LLMs and scikit-learn, focusing on avoiding shortcut learning, leakage, and handling imbalance.

Complete suite of 4 Python labs for network packet analysis: Basic parsing, AI-powered dissectors (Groq LLM), ML protocol classification (Random Forest), and real-time analysis. Built with Scapy, Scikit-learn, and OpenAI SDK.